{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.046815</td>\n",
       "      <td>9.741152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.408520</td>\n",
       "      <td>8.763270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.195915</td>\n",
       "      <td>10.853181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.914701</td>\n",
       "      <td>11.174260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.576700</td>\n",
       "      <td>9.042849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1         x2\n",
       "0  8.046815   9.741152\n",
       "1  8.408520   8.763270\n",
       "2  9.195915  10.853181\n",
       "3  9.914701  11.174260\n",
       "4  8.576700   9.042849"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#加载数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('anomaly_data.csv')\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据可视化\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure(figsize=(10,7))\n",
    "plt.scatter(data.loc[:,'x1'],data.loc[:,'x2'])\n",
    "plt.title(\"data\")\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "中心聚集的点是正常数据,旁边分散的点是异常数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#define x1 and x2\n",
    "x1 = data.loc[:,'x1']\n",
    "x2 = data.loc[:,'x2']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "plt.hist(x1,bins=100)#100个数据分割\n",
    "plt.title(\"x1 distribution\")\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('counts')\n",
    "plt.subplot(122)\n",
    "plt.hist(x2,bins=100)#100个数据分割\n",
    "plt.title(\"x2 distribution\")\n",
    "plt.xlabel('x2')\n",
    "plt.ylabel('counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "中心是最多的\n",
    "再看高斯分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.112225783931596 1.3559573758220915 9.997710507954396 1.3097071175386399\n"
     ]
    }
   ],
   "source": [
    "#计算x1 x2 均值和标准差\n",
    "x1_mean = x1.mean()\n",
    "x1_sigma = x1.std()\n",
    "x2_mean = x2.mean()\n",
    "x2_sigma = x2.std()\n",
    "print(x1_mean,x1_sigma,x2_mean,x2_sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#计算高斯分布的概率密度 p(x)\n",
    "from scipy.stats import norm\n",
    "x1_range = np.linspace(0,20,300)#生成从0到20的300个连续的点 \n",
    "x1_normal = norm.pdf(x1_range,x1_mean,x1_sigma)\n",
    "x2_range = np.linspace(0,20,300)#生成从0到20的300个连续的点 \n",
    "x2_normal = norm.pdf(x2_range,x2_mean,x2_sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "plt.title(\"normal p(x1)\")\n",
    "plt.plot(x1_range,x1_normal)\n",
    "plt.subplot(122)\n",
    "plt.title(\"normal p(x2)\")\n",
    "plt.plot(x2_range,x2_normal)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EllipticEnvelope()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "ad_model = EllipticEnvelope()\n",
    "ad_model.fit(data)#无监督式学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  1  1  1  1  1  1  1 -1  1 -1  1  1  1  1  1  1  1  1 -1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1 -1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1 -1 -1  1  1  1  1  1\n",
      "  1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1\n",
      "  1 -1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1\n",
      "  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1\n",
      "  1  1  1  1  1  1 -1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1 -1\n",
      "  1 -1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1 -1  1  1  1  1  1  1  1  1\n",
      "  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1  1 -1 -1 -1 -1]\n",
      " 1    276\n",
      "-1     31\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#预测\n",
    "y_predict = ad_model.predict(data)\n",
    "print(y_predict)\n",
    "#1是正常点 -1是异常点\n",
    "print(pd.value_counts(y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5CycnBy66CChn5eRu3dw5IiIiR5KffoKhQ10gu/12mD3bBbTNmyE9HS64AC67DB591FUrkCqhTzqc6GgoKAg8LLNyckGBO0dERORIsWMHnHOOC1+ffuoWu9Ws6V5r3hz++lc3xefrr93PR7CzzjqLskptffbZZ6SkpJR6zMKFC5k5s/J3llQ4C6dzZ5gzJ/CwzMrJX3zhzhERETlSPPmkW9Q2bhyUtDKzUSPIyHDTfVatqtLmRSKFs+p07bVukn9ubrHKyWseHkJqcnzhHLSVK92Y/CWXVHerRUREyic/H55/vnzVBho3dvOwn3vuoG9z4YUX0qtXLzp37szUqVMDz8fGxjJu3Di6detGv3792Lx5MwBr165l4MCBdO3alYEDB7Ju3ToARo4cyU033cSAAQM44YQT+Pzzz7n22mtJSEhg5MiRgevedNNN9O7dm86dOzNhwoQy2/fBBx/QqVMn+vfvH9g8HeCbb74hKSmJHj16kJSUxPLlyzlw4ABpaWm8+eabdO/enTfffDPscRXCWnvU/OnVq5etMCNGuD8+n33so+X2vveWWJ/PZ6211ufz2fveW2Ifn/GDtQMHWjthQsXdV0REpAJkZWWV/OK8edZ261b+iy1dam379gfdhm3btllrrd2zZ4/t3Lmz3bp1q7XWWsC+99571lpr77zzTjtp0iRrrbUpKSl22rRp1lprX3zxRXvBBRdYa60dMWKEveKKK6zP57PvvvuujYuLs4sXL7YFBQW2Z8+e9vvvvw+5X35+vj3zzDPtokWLrLXWnnnmmfbbb78NadvevXtt69at7YoVK6zP57OXXXaZHTp0qLXW2tzcXJuXl2ettXbWrFn24osvttZam56ebm+++ebANUo6Lpxw3wcw34bJM1oQUJKnn4Zzz4Wrr2bMI49g23QIrZx8UhTmlltcl+/48dXcWBERkYOwfbubV1ZeLVocUsmoKVOm8M477wCwfv16Vq5cSZMmTahZs2ZgflevXr2YNWsWAPPmzQv0YP3hD3/grrvuClxr2LBhGGM45ZRTaN68OaeccgoAnTt3Jicnh+7du/PWW28xdepU8vPz2bhxI1lZWXTt2jVs25YtW0b79u3p0KEDAL///e8DvXu5ubmMGDGClStXYowhLy8v7DXKe9zB0rBmSerVc6tWWrSA7t0xF14IEya4IDZwIGbAADjrLHjzTS0GEBGRI0tsrFsQUF7bt7tzDsJnn33G7NmzmTdvHosWLaJHjx7s27cPgJiYmECHR3R0NPn5+WGvEbxLQa1atQCIiooK/Ow9zs/PZ82aNTz66KN8/PHHLF68mKFDhwbuV5KSdkEYP348AwYMYMmSJWRkZJR4nfIed7AUzkpTpw5Mngzr1sHFF7vnatSAP/7RPTd+vIKZiIgceXr1gh9/hDVrynf8W2/Beecd1C1yc3Np1KgRdevWZdmyZXz11VdlnpOUlMQb/rqhr732Gv379y/3/Xbu3Em9evVo0KABmzdv5v333y/1+E6dOrFmzRp+/PFHAF5//fWQtrdq1QqAadOmBZ6Pi4tj165dZR53uBTOyiM21m2Afv/9rvfs8ssLlxuLiIgcaerWdX+vPfVU2cfu3+8WD4wefVC3GDx4MPn5+XTt2pXx48fTr1+/Ms+ZMmUK6enpdO3alVdffZUnnnii3Pfr1q0bPXr0oHPnzlx77bUkJyeXenzt2rWZOnUqQ4cOpX///rRr1y7w2l133cU999xDcnIyBUGltQYMGEBWVlZgQUBJxx0uY4+iLRp69+5ty6pjIiIicizIzs4mISGh5AN++gn69IF//AOuvDL8MQcOwO9/7wrQaiecwxLu+zDGfGet7V30WPWciYiIHItatYL333flNEaNcgVnPXl58Pbbbkun/fvdbgFSZbRaU0RE5FjVtSt8950bthwyxE3ZqVcPNm50xdXvuMPtIKD51VVK4UxEROQoZa0tcUViwHHHuQVu99zjFgjs2QPNmsHxx1dNI48BBzuFTOFMRETkKFS7dm22bdtGkyZNyg5o4KoR+Gt+ScWx1rJt2zZq165d7nMUzkRERI5CrVu3ZsOGDWzZsqW6m3LMq127Nq1bty738QpnIiIiR6GYmBjat29f3c2QQ6DVmiIiIiIRROFMREREJIIonImIiIhEEIUzERERkQiicCYiIiISQRTORERERCKIwpmIiIhIBFE4ExEREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgiiciYiIiEQQhTMRERGRCKJwJiIiIhJBFM5EREREIojCmYiIiEgEUTgTERERiSAKZyIiIiIRROFMREREJIIonImIiIhEkBqVdWFjzEtACvCLtbaL/7k3gZP9hzQEdlhru4c5NwfYBRQA+dba3pXVThEREZFIUmnhDJgGPAW84j1hrb3C+9kY8w8gt5TzB1hrt1Za60REREQiUKWFM2vtHGNMfLjXjDEGuBw4u7LuLyIiInIkqq45Z6cDm621K0t43QIfGWO+M8aMKu1CxphRxpj5xpj5W7ZsqfCGioiIiFSl6gpnVwGvl/J6srW2J3A+cLMx5oySDrTWTrXW9rbW9m7WrFlFt1NERESkSlV5ODPG1AAuBt4s6Rhr7c/+//4CvAP0qZrWiYiIiFSv6ug5GwQss9ZuCPeiMaaeMSbO+xk4F1hShe0TERERqTaVFs6MMa8D84CTjTEbjDHX+V+6kiJDmsaY440xM/0PmwNfGmMWAd8AM6y1H1RWO0VEREQiSWWu1ryqhOdHhnnuZ2CI/+fVQLfKapeIiIhIJNMOASIiIiIRROFMREREJIIonImIiIhEEIUzERERkQiicCYiIiISQRTORERERCKIwpmIiIhIBFE4ExEREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgiiciYiIiEQQhTMRERGRCKJwJiIiIhJBFM5EREREIojCmYiIiEgEUTgTERERiSAKZyIiIiIRROFMREREJIIonImIiIhEEIUzERERkQiicCYiIiISQRTORERERCKIwpmIiIhIBFE4ExEREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgiiciYiIiEQQhTMRERGRCKJwJiIiIhJBFM5EREREIojCmYiIiEgEUTgTERERiSAKZyIiIiIRROFMREREJIIonImIiIhEEIUzERERkQhSaeHMGPOSMeYXY8ySoOfuM8b8ZIxZ6P8zpIRzBxtjlhtjVhlj/lpZbRQRERGJNJXZczYNGBzm+cnW2u7+PzOLvmiMiQaeBs4HEoGrjDGJldhOERERkYhRaeHMWjsH+PUQTu0DrLLWrrbWHgDeAC6o0MaJiIiIRKjqmHN2izFmsX/Ys1GY11sB64Meb/A/F5YxZpQxZr4xZv6WLVsquq0iIiIiVaqqw9mzwIlAd2Aj8I8wx5gwz9mSLmitnWqt7W2t7d2sWbOKaaWIiIhINanScGat3WytLbDW+oAXcEOYRW0A2gQ9bg38XBXtExEREaluVRrOjDEtgx5eBCwJc9i3QAdjTHtjTE3gSuC9qmifiIiISHWrUVkXNsa8DpwFNDXGbAAmAGcZY7rjhilzgD/6jz0e+Ke1doi1Nt8YcwvwIRANvGStXVpZ7RQRERGJJMbaEqdzHXF69+5t58+fX93NEBERESmTMeY7a23vos9rhwARERGRCKJwJiIiIhJBFM5EREREIojCmYiIiEgEUTgTERERiSAKZyIiIiIRROFMREREJIIonImIiIhEEIUzERERkQiicCYiIiISQRTORERERCKIwpmIiIhIBFE4ExEREYkgCmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgiiciYiIiEQQhTMRERGRCKJwJiIiIhJBFM5EREREIojCmYiIiEgEUTgTERERiSAKZyIiIiIRROFMREREJIIonImIiIhEEIUzERERkQiicCYiIiISQRTORERERCKIwpmIiIhIBFE4ExGRY4O11d0CkXJROBMRkaPXwoUwahQ0aQJRURAbCxdeCB99BD5fdbdOJCyFMxEROfoUFMAtt0BKCrRrB4sWuefWr3fP3XUXnHMO7NhR3S0VKUbhTEREjj633w5LlsDSpTBuHLRu7XrOGjWC66+H776Dk092QW3fvupurUiIGtXdABGRI8J330FmpvuLvHlzGD4cGjas7lZJOAsWwP/+58JZgwYAWGsxxgQOsVFRmKeegsGDYdo0uPHGamqsSHHqORMRKc306dC3L1xyCWRnw+bN8O670L493HADbNlS3S2Uop591oUtfzCbPGsFE6dnYf0LAqy1TJyexeSPV8Gdd7rjtVhAIoh6zkRESvLMM/Dww/D00zB0KERHF762aRM88gicdhp89pkbNpPI8PbbsHgx4ILYzn15pM/NASAtJZGJ07NIn5tDanI8dsjZmG3bYNUq6NChGhstUkjhTEQknE8/hYcegi+/hPh4NywW9LJt3hwzeTK0aAEXXADffuvmNEn18vncJP/jjwfAGENaSiIA6XNzAiEtNTmetJREN9R5/PHw66/V1WKRYvSbREQknEcfhQcegPj4kofFZq1wq/4KCuCTT6q5wQK4gFynDuzaFXgqOKB5AsEMIDcX6tWrylaKlErhTESkqJwc+OYbuOKKkGExL6B5w2I79+VhAUaPdvOWJDKcfTb85z+Bh953FiwQthcvht273cpNkQihYU0RkaIWL3aLAOrUwUDZw2IDBrj5ZxIZRo+GsWNh5EhsVFTIHLPgOWcAae89iRk1CmJiqrfNIkHUcyYiUlReXshf1mUOi8XEuHMkMpx3nlupeccdGGupXzsmJEynpSSSmhxPvznTMR9+CDfdVN0tFgmhcCYiUlTbtq5sRpE5ZsGC56CRnQ1t2lR1K6UkUVHwzjuuNt3QoYyJ3kDa0IRAmDZLl5KW8QTnvfk0fPABHHdcNTdYJJTCmYhIUb17u7IZn38eMscsNTmeNQ8PITU5PmQOGs89B6mp1d1qCdaokVukMXw43H47Jj7eDVUnJMB552FatYL58yExsawriVQ5Y4+iwnu9e/e28+fPr+5miMjR4Nln4fXXYfZsJn/uJv97w2JeYKtfO4Yxdbe4jbRzcrTiL1JZCytWuHIZ9eq5gKY5ZhIBjDHfWWt7F3te4UxEJIyCArj0UvffV1/F1q8fuv2PtZhPP4WrroJXXnHznEREDkJJ4azSVmsaY14CUoBfrLVd/M/9HRgGHAB+BFKttTvCnJsD7AIKgPxwDRcRqVTR0fDmm24D7fbtMVde6TbJrlsX1qzB/POfsG4dvPYaDBpU3a0VkaNIpfWcGWPOAHYDrwSFs3OBT6y1+caYRwCstXeHOTcH6G2t3Xow91TPmYhUip9+ghdegHnzYO9et/H51VfDsGFQQxWJROTQVHnPmbV2jjEmvshzHwU9/Aq4tLLuLyJSYVq1gvvuq+5WiMgxojr/yXct8GYJr1ngI2OMBZ631k4t6SLGmFHAKIC2bdtWeCNF5Bj2ww/w+eewZw80bepW/jVtWt2tEpGjXLWU0jDGjAPygddKOCTZWtsTOB+42T9EGpa1dqq1tre1tnezZs0qobUicsz5+GM4/XQ4/3xYsgR++QU+/BA6dIARI9wwp4hIJanynjNjzAjcQoGBtoQJb9ban/3//cUY8w7QB5hTda0UkWPWK6/A3XfDlClw0UWhc8q2bYPJk+G001wNrZNOqth7L18Ozz/veuzy8yE+3tVPO/10CFopKiJHtyoNZ8aYwcDdwJnW2j0lHFMPiLLW7vL/fC4wsQqbKSLHqm+/hbvugs8+g06dXLmMoJdt48aYBx5wuwEMHep61SqiXtaOHTBypFtwcN118DGavb8AACAASURBVJe/uFD4ww/wxz9CzZrwxhuuPpeIHPUqbVjTGPM6MA842RizwRhzHfAUEAfMMsYsNMY85z/2eGPMTP+pzYEvjTGLgG+AGdbaDyqrnSIiAZMnwz33QKdOTJ61ImSLJq/w7ORZK1xgatEC3nvv8O+5axecfbYLfOvWwUMPuZppAwfCHXdAVpb774ABbpsoETnqVeZqzavCPP1iCcf+DAzx/7wa6FZZ7RIRCWvLFnj/fXjmGay17NyXR/rcHMBtch68hZO1FjN6tNtF4JJLDu++48ZBt25uGNW/+0BIsVvApKaCzwe/+53bL1JDnCJHNRXoEREB10PVpQs0bIjBBTKA9Lk5gZCWmhwf2MKJAQPg5psP7567dsG//gWLF4MxTJ61ouRtolJT4ZFH3NBnUtLh3VdEIpo2PheRI1NeHqxe7ULV1oOqVx1efn7I5H9jTCCgeQLBDNxcs7y8w7vnO+/AGWdA69YhvXXecKrXW7dzXx7WGBg1CtLTD++eIhLx1HMmIkeWjRvhuedcxf6YGLed0saNbgXl6NFui6VDGfZr08Ztju0PaV44CjZxelZhQMvKcuccjnXrINEFwOAwWGJvXefOMHv24d1TRCKees5E5MjxzTfQs6erO/bRR7B2rZskv3Gj24B87FhXeiI//+Cv3bEjtGsHM2aE9FqlJsez5uEhpCbHh/Rq8fzz7l6HIyYmpK1l9tbl5VXM6lARiWgKZyJyZPjxR1eh//nn4dlnsZ07F75Wpw72D3+Ar75yBWLvuOPQ7nHLLTBxImbvXurXjgnptUpLSSQ1OZ76tWMwCxfC9Omu/MXh6NbN1UsrsiI0WPCKUT75xJ0jIke1Stv4vDpo43ORI8yaNfDf/7rirnXruvIR/fqFH5a8/npo2xbS0kqfON+3BZx4Inz9NZxwwsG1x9rCHQDeesvVNQteOWkt5uuv4eKL4cknD3+lZkGB23XgjTewp54a0ltXdIVo2oB2mHbtYMEC18MnIke8kjY+V8+ZiFS9ZctcEdc+fVyPWN26buXiNde4Ycvp00OP374d3n4bbryx7InzcXGuR+v55w++Xca4Cfc9e8JJJ2Guv9615bPP4NVXMWef7XYNeO65ww9mANHRruDsjTdidu0qubeuZjTmtttg8GAFM5FjgHrORKRqLVjg9qwcNw5uuAHq1Cl8zedze1jecAPcf7+rlg+u2Oszz8AHrh51cCDzhEycnz/fnbto0aG385df4MUXYc6cwo3Pr7gCLrzQVeyvKNbCrbfC3Lnw9NPYfv0wUYX/brarVmHGjnW9eR99BPXqVdy9RaRaldRzptWaIlJ1fvvNzRt79lk3NAihRVejorCDB2M+/dSVmOjaFU49FXbvhoYNA5fxepWCw1nIxPlGjVxP3GGwzZph7rnH7RhQtJ3eMUULxoY5pkzGuCHSqVNhxAhMvXpw5pmurMeSJZgFC1zQfPnl0CBbUfcXkYijYU0RqTqvv+6GDP3BrMQtknIs3HknPPGEO69hQ9eT5VfmxPnNm0PC3MF67KPlIdfz+XyFWzf5lbq908Eyxm0JtXw5PPqo2/C8RQsXytatg7/9rVgwq9D7i0hEUTgTkarz/PNw000AZc8dGzHCzffats31JC1aBOvWla/MxSuvuLlhh2DyrOXMzt4cuJ7P5yPlyS8L22Vt2W0/1OkiUVEwaBD2jjtcOL38crcStcj1Ku3+IhIRNOdMRKpO3bpuD0v/vKky546deio8/bRbOHDHHa6HafLk0ldrnlIfEhJckdiWLQ+qecHtSWwZR9bGwqHRxJZxTL+1P1H++WBltv0QlfrezukYtq0VeX8RqTparSki1a+gwK1Q9Cuz6Gp0tDsH4K9/ddsdPf88Y87pGHKcd50x3RvBkCHwpz8ddDALvk5qcnxIMANCglm52n4IDqZHrDLuLyKRQeFMRKpO+/awcGHgYalzx/buhZUrXW0zcHOwZs2Cv/8dUlIwH3zgVncCbNqEefBBV6B1yBC4995DbqIxhvFDE4o9HzKnjcJ5aKUdcyj39sJh+twc2t8zk/S5OYxMahcSvLyh1Yq+v4hEBq3WFJGqc+21rkZYv37F5o4FF10FSNv6DaZvX2jVqvD8Dh1g8WK3sODee2HYMKhVyw13/u53MHMmdO9e7uaEW+1orSXlyS9DjmsaW5NpmWsBmDCsc+CYrI27wrf9IHqwwq2wLLoS1RDaxvszlrJwfS4L1+847PuLSORROBORqnPttXDyyfDtt5hTTw1bdBWg2YHfMA89BFOmFL9G3bpuFeN117m9Jvfvd3PYDjKMhJvb5YWerI27AnPMJk3PJj0zB4Dv1+0AYNKM7MAx44cmhLS9fu2Ycgej0toQzLt/2jAXwqZlrqV7mwakJhV+duOHJoAtvL/Xg1ZmWw4ccAsvli93jzt0KAy9IlIttCBARKrW//7nykb8+99w9tnFe69Wr8Zceqnbyunvf6/w23u/8wK9dknxgdCTPjeH7m0a0KNNI8anJBAVFeV6+DKy+H7ddhZuKAxNqcnxjB+aEDIPraQ6Y+F66ELa4A+o92csDfTQee0Kfi743sHzzR6fvZLPV/xC99YNSRvmwpoX8s7s2CxkIUFAfr4r0fH009CpE/Tt656fPx9++MGtqh03Thuti1QiFaEVkchwwQVQu7bbK7NJE8zIkW7ocudO+O9/MV984Sb//+UvFX7rxz5azq79+aSlJJKWkoj1WdIzcwI9U8GhJ2SxwTD3XPt7ZgauFW7oMFwwK231pXev9Lk5gSFJLxx6IWvCMLfBe3BA8641edYKdu7Nw2d9/mHOXL7J+ZWY6CgW+YNkjzYNi4fG/Hy49FK388HHH0Ni6MICli+HMWPcd/XuuxW7I4JIJMrPh4wMVyB7/nzXo3z88fCHP7jfVYewwOhwaEGAiFS9885zk/3vv9/9InzpJXj/fbff5rp1rsZXBc+ZKlq/zFrL1zm/hhzjhZ5wIau8k++9eWvez7l7D5S4+tK7Z7B3RicHgpkneM6Zd2+fz+dWdmbmEGWiGHGaWziRtXFXIJh5vW/F3s/48W44eMYMSEwsXketY0e3ZVZMjAvKIkezdeugRw/XUz9iBKxYARs3urmtP/0EnTu731FVSD1nIlI9oqPdysohQw77UmVtY+RKVOQH5okF91QFmzg9q1iPWJkLF4KOnzxrBZ+v+CXQ8+Uu4BYUBN/Tm6sGcH/G0rBtCLl3Zvh7e9cI916AQDAL+Tx278ZOnYrx7zs6ffwTHLfga049rhamWTPs5ZczcWMdV1ft+eddzbgJE6BBg1K+AZEj1JYtcPbZcOON8Oc/h/6jsFcv92fMGPcPyho14JprqqRZCmcickQrT9HW4An7RYPMyNPaMWF45xIDlzGmxIULwZP/vV4yb3gRwGd9vDxvXbE2Z23cxaTp2SGvpybF47O+Ym0o7d5RUVHFVnYGm5iRRf06Ndi5L7/w8/n3v1nWsQc///1Fzv6/5+nSpBWvHNeDTbGtGFa7gNyhF3BBdCzf3DkJO+gyzHnnwauvwi23HMK3IxLhJk1y/0D0T6MI+w+9k092vcynn+62nouNrfRmKZyJyBHH+wUaXLQVCOlZSk2OD/lF661oLBZk/L+HS1ttOeacjmGvFbwYAAiZH+bNYwNIaBlHdpHdBoJfT02KZ3xKApOmZ9O9TYOQNhS9t9fW4NWdwRJbxnFqfCNenreO9MzQnQ7SUhL59r3P2btpO6e88yp88AFtu3TBzsjmtrk53HYAokb05tH8LG546GZMn1Zu66yg2nQiR43du+G119zWcJT1D73OMGCAO/6Pf6z0pmnOmYgcUYI3/PZCkjdU6RVtDbeNkbdHZrCElnFMy1wbmE+WlpIYfmUjoZP9J89awaQZ2cU2HX989spAQAuW7R9OBYptCwW4YDbDlezo0aYRdwzqEPbe3nsP3DPDldVoUb8W3VrXJ6GFu7Y3B61b6wYMSmgeUtR2d/YKTt22mmbzM+GUU5g0PZu4WoX/TvdFRXPRP+7GvPoqXHKJ26EhP7+Ub0TkCPXRR26LuNaty7c7R2oqvPlmlTRN4UxEjhjhfoF6NceChZs3FlybbPVD55OaHB8ITXG1ogMLAYIn9HvnBv8c0oaMIr/E9+Zx33tLwrbdK1ibcUtyIKh5Thj7fmGoDDeBP8x7B/h+/XYABndpwZkdj6Nv+8akJsVTv04ME4Z1pmfbRsW2eYrfvpHN7TpAs2Zc9Ewm6Zk5zM7eHHKvi57JxJ53niuv8c470K5dqd+LyBFp61Zo3RooeXeOkH/otW4N27ZVSdM0rCkiR4yic8eCJ9gHB7SJGVnFQo43d8sbjvSus/SnXHbtL8Bay+OzV7Jzbx4WS4M6NbljUIfAsAYQGPJIS0kES2gZDv+csaJzyILnnN07pBMPzFwWCIkZtyRz4rgPAq/H1owutWhsuPfube00aUY20+atZWRSu0C70zPd1k8TM1yYO37nLzTam4tZmUuXP/2H32rVBfzBMSkei2Va5loWrt/hPsORIzGXXQYvvnhwX5TIkaBuXTe06ef9fgme+hDyD73du905VUA9ZyJyRAm34bfXK7Xm4SF0b9OQ9MzCXq3gPSjTUhID88S8IdHOrRqQPjeH+zOWsnOvK00xLXMtuXsPMDGjsEcsuCQGgCW0/ETasEQa1q0VqNw/PiWBKBP6K/aBmcuIqxUdCJPDnpob8vrHy37B5+0XGiR4OLPoe58wrHMgbKYmxzMtc23InpwGE1jtOffSduSe2Ikv47tz6zw3PNOttVuF6b3vkUntAr1vZu5ct3otPr7c34/IEeP002H2bFfvjzL2+gX473/dHMwqoJ4zETmihPsFGlyaonubBixcvyMw5Be8QKCo4B60ogsFvKKvI5PaBcpiGGPKLMNx+8CTAAJzyAKT/WdkB9rx3s1JDH86M6S3zwtsk2ZkF9vkPHfvAaZlrnXz7IrWPAvqJSz6r/4Jwzrz+OwVhcV1P90MWNLOvYn//OtO9kfXZOef7wrURfPOIT8f88gj8J//VFlPgUiVa9cOkpLgX//C3nBD6SVzzmqLeeUVV5exCiicicgRo7SaY16oCV4x6VX0D7dAwBMu1IS8TsmrPbu3aUD3Ng1d79TcHLAEhkRLK4FRo0YNzklsHhLOpt/an0kzsoutFn189koMhhGntQ3ZJaBb6wb0bNvIDasaV/Ps4mfnhbR9YkYWPuujQZ2aWGt5IsfHiNUruXhCR9qP/Y6U8y8mdsRA6nc9l4XHnwzA57+byZlz/gcdO8IDD8CTT5bvyxE5Et17L6SkYHr2pH7t+mH/P9soymKuvtqV0aiiXmSFMxE5YpS35tiEYZ3DbncUTrieuGDenLLxKQnFVnvuzysIGQr8fv12Fq7PLXkbqKDisjv3ha6AnDQjO+xend4uAKlJ8SHH92zr9v/EQFytGkyans3C9W5j9q6t6pPn35oKXJDbuS+P7Lx6DO3cjXG7f4DmvXj1/qnM/d8cblv9Gbf99g2rNu/iS9OI7Luf5MZbLsRccAGMGlXiZyNyxOvTB154Ac4/nzFjxmCvu67w/7M+H2msxkx60C0GeOqpKmuWNj4XkSNOaTsCBPeueYr2nAXXSfOODZ6fBW44EwvT5oVuOu5N5J80I5tpmWuLLUYorZfO2wvTm3jv3TM41BUNaD6fj0nTs0Pqonnt83oJjTE89pHbnqroylUorLOWmhzP+JobiLrxRvjqKyYvziV37wEmDOtcvK5T3iq3p+CqVRralKPfkiUweTK8/TZ06uT2/121yu37O3q022MzquKn6Ze08bnCmYgcNUob9vQePz57ZUihycc+Ws6srE2c27kFYAIbiTesW4s7BnXgwqfnhszJ+vHBwf6J/TXYuS+P+rVjmPLJqsDrax4eUmIpDK8t3ds0pHubBoWT9f0rJbM37iLx+PqhRTAzsliw7lcWbdgZuNbIpHZMy1xbLAgWFBTwwIxlxYIcFAmNkya5Ypqvv47t3j006OblYV57ze1vmpEB/fpVwDcjcoTYvh2ys93es8cfDyefXKm3KymcaVhTRI4aZQ17AsV2FJizcgvZm3bT94Q8JgzrHDTM6VZ6HigIXT2Z8uSXZG/a7e9Zs8xZuSXk9YnTs8IOTxYtA+INQXpt9WqxFdvtIDOHprE1Q98nhpFJ7YirVSOkQG3u3gNQwr+3Q3rzxo+Hli1h+HBM27Zw0UUQFwfr1rlJz23bugKdPXqU/8MXORo0auQWCVQz9ZyJSEQoa/Pygzn+YIc9wdUlSxuWyEXPZLJw/Q4SWsTS94QmTMtcS8M6Ndixt3COWEKLWPq2b8w0fw0zb4jRu6439BkdHR24X1ytGvzp3JOx1gYWKkBoT1u4tnnDpiGLD/z1y4BAPbb7M5aGzLMrymtjyGean+96xz7/3JUTaNYMLr8cunUr8ToiUnE0rCkiVeunn9yfmBjo0KHUzYLLs3n54RxfNKz5fD5OGPt+4HFqUnzYocDurRuwv8AXsi9mUQktYplx2+lERUXh8/kY+uSXgZ0Hpt/an4nTswJz0wYlNGfX/vwy58MFh7fbB57Ezr35pc5T83re+jz0MVt3Hwic26huDNv35NE0tiZbdx8odT6ciFQ9DWuKSOWzFt57z61q+v57aN8eDhyA9evhyivhttvcZNuQU8q/efmhHO9NwvdqgYXbY7NoQdkRp7Xl5XnrWBg01ywcrzq/N4T5xMer6BPfCKwla+OuQACsXSMqMFHf2x2gaFDzhjyLrhzduS/frcp0DQ3pHRt5WrtA2DLG8Lu+bXni48L5b9+OPZsHZy6nfp0a7NyXT/3aNQJh9mB6KUWkaimciUjF8PngxhshMxPGjoXp06FWLffaTz/Bc8+5ZeuDBrk5Ta1awVVXQatWYbclCu7lCQ4PJW3h5AWl4B4ogM9XbAnM7/LKYWRt3EXT2Jp8M3ZgoGcrWNHK/gDX9GtDVFRUsWO99oGbz/byvHWMTGpH9qbCbWH25ftIaBHLoITjABfQ+p7QJFA415sPV9Jihu/XbWdj7j4a1w2dezb9h41kb9rFm388LWx5juFPZwaGV30+H5NmZHPF8/OKLzoopddRRKqewpmIVIyxY2HZMvjqK4iN9Vezx/WmvfEG9p//xHTs6CaaX389/Pgj+xK7sPqUPiS8lV6sEOz4oQklhodwhWODK+d7QaR+7RqBHQOC98EEGHpKC/eDv9Ose5uGvDM6ifvfWxp2iDNj8Sa278kLeS44qDWoU5PxQxOw1oad+5W9aXcgsBVdsOAFynCLGay1fL92O5t37mfzzv0h19y6+wDNYvPcKs2ZywLBbvzQwhA67Km5gQK33ny4kF7HjKzA9k7qQROJDJpzJiKHb/NmN1y5ciU0bVo4J2xoAmb0aOz33/PMiHEc6NCJMeu+gLffxs6Ywd/e+pa6UyYzYtUc0h96mSdWFoaPhJZxzAgKFUV70kqa1B9Xu0ag3ldqUjz3Du0UWGHpGXFaW+4b3oXHZ6/k8+W/0K1NA+4b3gVrLUOnfBE4NqFlHH3jG4fUOjv5uLos/2UPCS1iyd60OzCfq3ubhlifj/0FPpYF3Suc1Q+dHxgK9X4He6HI5/MVW+lZtF3BRp7WjgnDOxcrEeIN3wbXPevepiHdWtcnykSFBFAvmCqYiVStkuacaeNzETl8L74Il14KTZuGzAnLuH0S9ttvefjPT/H3ddHk7j2Aveoq+PZbzOrV3H1Zb7bfPY6nOg9mwPibwVpGntYuUDT1hLHvlxrMvM3OvZWL6Zk5TPlkVWDY0ju2aKjxrvP5ii0s3JDLN2u2u1IW07PJ3rSb6ChoUi+G7I27AsGsbs1ouraqz/t3nElqcnzgmlt3HyA1KZ5ureuz6KedYYNZzejQ0JPy5Jf4fD63oXlGFvdnLGXyrBWBchqTZ60IaWtUVBRx/qHPYJ1axJK9aRfGGMac0zGkFy4qKorpt/YPOb57mwa8PG9dsTl23ds0KNfXLCJVQ8OaInL4Pv7YFS0laE6YtSRcexNXnjuarxf8Eii6Sp06MGQI9pNPmNQiiQZ1Yphy6oVctehDev2UzYThQ7HWhqymDA4d4WqZBe+n6dm6+0Cx6v6eaZlr+X7dDrq3dkOe2Zt2hdyvwOeGPV/5an3guSX3nRuYeF90SDVtmBt+/GbNdrI3FV/ZeaDAktAilnMSWwR69YZO+SJQqgPcnDlviHHEaW1Dhhjz8/PZuS+v2HWXbdpd7FiPF/RCn6TYHp0QOiQsItVPPWcicvh274YGhb0vxhjSGm3HYvi6TRcAerRxm3RPnJ6FrV+fD7/5kfS5OczK2ow1UbzWfQhXL3yfiRlZxVYsTpyeRfAUjDsGdQgJbD6fr1wBY8RpbenUwpX0+Gn7HsanJDDytHZhj4uOig55zgs64fbi9B7XrFG8DV1b1adpbE3OSWzOn849mem39iexZRzZm3aHhKRpmWtJz8yheVwt3lu0kcmzlgOu6v+pD31S4lCp1+vntcvrgSvau9i9TQOmzVvL16u3hZzfrVX9wu/lKJrmInIkUzgTkcPXuDH8/HPgobWWjLc+4fvjTwZv9STW1RObm8MHHy3gw415gQKrqcnxjH8glb4HtpKemROo57Xm4SGkJrtzvPAwedaKkHD0jw+XceqDHwcmta964Dxq1Qj/q+3leetYtmk3Jx9Xl9aN6nLJc/MCKzmDBbZV8gcbrw33Zyzl/oylpM8trNqf0CKW9Lk5nPrg7JAtljwrNu9m6+4DzM7+JTCfLOOW5BI/ygMFPrbvyeNfX62joKCAYU/NZfuePKKjoFPz4rXift3j6pp5YczrYQvuXQQXjgFWbdkTcv667Xvp1rpByMbxIlK9FM5E5PBdcgm8/DJQ2LM0b+VWOrSIC4SbaZlrsVga7cklee0iPj2xN4MSmhcOT0ZH06pBrcC+k141+7SURFKT44ttvzRxehY+n4+Pl/3Cr3vyaFw3hnHnn8zwpzPZn+8rrbVERUezcEMuizbsDFvLbPoPG0NqiKWlJNK9dQO+X7edBnViGJnUjhT/ak9v7tm23wqHHRvVLZwfti/fRy1/nbNJM7Lx+XwMe2puiW3bviePRnVj+HVPHieO+yBQF235xPPoe0LjkGM7Na/H5p37w87NC56DZozh3qGdaFQ3hnxfYe9YY3+R2gMFPm4feFKpn5mIVB3NORORw3fVVXD33bBkCaZLF+rXjiG+fw96/HcuBgIlIRauzyV1/nt81KEfO+rUZ9f+/EDJDBYswJx4Iu+MdvvaFa1r5j0OV+OssT/MnHTvh4CbgH+goDCENKwdzY59BYHHXsX/pvVi2OoPVQktYunTvjEvz1vH1t0HQibNT561nA3b97D1tzx6tG3EvUM6FStkC1Ar2rC/wBYruXFD/3h+y/OFtDncSlDP8G4tedm/NRRAxi3JPDBzGS/PWxdYJer1OgYLfJZ+wT9HR0fz+75tefLTHwPP/bonL1AQN3iFaDFLl8Ibb7hVubVru70HL74YatYs+RwROWTqORORw1evHkyeDCkpsHIlY87pyDX3jMDk5sK33wJuqLDDjP9j5IrPuGT6i4GhwkkzsrE+Hzz7LFx/faCnJ1jRwOEFNM+vRcLQgQJL09iajExqR0KLWHbsK6BT83rFmu0Fs07+LZjuG96Fkae1o1vrBjSsWwtjDJNnLWdW1ubAsdMy13LSvR+ybPNvxa53ZZ82YT+e3/J8gYKznhm39qdB3RgSWsTRpF7oSsw3528IeTzsqbnE1oyme5sG9D2hCSOT2hULZuDmxZU0b8xay+4DBcWez7glmT+de3LYc8jOhrPOcoWDDxyAXr3crg8vvOAKCT/xhKtjJyIVSj1nIscCa2HePPj6a9i/H1q2hAsvDJnEf9iuucZdu18/+N3vMDfeCDffDH/+M+ZPf+KahybTcP0a4uZ8gmnXjrS27i/1+rVjMNOmuU24zz67HG+l+IT8WjWiQoYya9WI4qu/DiA6OprHZ68ANlGzRjQl6dveDRcaY0gblhi4vld1P3vT7sA+laUJ7u3yNI2tSfrcHL4qMhHfm0Pnre5MTYontlYUU+esYV+ej8Z1Y/hm7Nn0eegTsjbuwgJ94hsF5uMFC5QSCSouW3R7puBN2YODnVektljP2eLFcM45MGGCKxoc3Es2ZgxkZcHVV7utuR59tNTPRUQOjnrORI52//d/0KMHjBwJa9bAr7/C//4H8fFuu6Vt28q6QvndcIPbU7N+fTjvPPjLX2DuXEhNpf3vL6Hh6uWYRNfrZYwh7bwOjFn+EYwbB2+/DaUNrUGxVYg/PjiYxnVjis0x25/v48H33WrHOwZ1pO8JTVjkn1s24rS2gcn8npfnrWPolC947KPlTJqezbTMtXy+YgvghgoTW8aVGsz+0Ld1SC2zhJZxgZ66rbsPUDsmiuyNuxiZ1C4QpKZlruXleevo1ro+qUnxjE9JYPcBH/sLLLVqRPG7vm158P3lgfl0cbVqcN/wLqQmxYfdgSB4bl64nse4WjVCFmCsfuj8wONiPW55eXDBBa5nbPRoqFmzWI+cTUiAzz6DmTPd/8ZEpMKo50zkaPbII/D8825fy0GDQsPPxo3w0EOQnOz+km3RomLu2bYtPPCA++PzQUEB3Hsv3HcfJjMTzj3X/WW/fDkmPR1OPBHmzMGedFKJxTC8Ol6BGmdJhRPfr+7Thqc+Wx1yfGLLOOJqFW7y3aBOTbq3bkC3Ng0wxrB1t1vh2KReDAbD1t8O+LdXKtw03CvMaozhvZuTAvPZimpYJ5ro6GgOFFiiDRTYwjltCS1iqVUjmp9z97Ivz90zLSUxJFy9Mzo5pH4auB4wb25YanI89w7pRHR0tCuZUaSA7MikdkzLXIsxhvFDE0qcO/anc0/GGOh7QpPAZ+dt61Qs0L37rvser7wSoHDHh3D7cT74oOs5u+yyEr49OxBouQAAIABJREFUETlY2r5J5Gg1fTrceqvbiLxly2KFSgOP77/f7Xf55ZeBshcVzVqL2bkTXn0VvvuO7LVb2d6wKafdNwbTtWupm2+HCwb3ZyylQZ2a3D7wpGJbFHmLA7w9JifNyCbr550ktIwjLSWRJz5eSe7ePL76cWvYeWPgAs+EYW5LpNy9B0o91pOaFI/P+kKGNlc/dH7gM78/Y2nYHq/gFZbeZ9X+npmB19c8PAQo3NXgomcyQ8p/pCbFY3EBtLSNy73vu6T/hjj3XLj2WrjyymK9lcEbsqcmx5M2uCOmfXv3v6HExPA3F5GwtH2TyLHm7393PRotWwZqg3n/GAsuWMr48bB1q5uTVgkC965fH265Bd+LL/LnC+7k6o6XMnFtND6fL6RGl89XOEQZvBWU1/6J07OYlrmW3L0HmDQ9O1Bq4scHB5PYMi4wDFgvJiqwL+eufXlMy1zLxc/OI3dvHlhKDVsGF1p27nXnLdv8G7VLqJ3mSc/MKTbnzCtcG24Rw+qHzg+p4ebz+fzhK7TMxv0ZS5mYkcVjHy1n4vQsFq7fQWpSUP21zBwMhjsGdSj7OwgKZN73H7a22YoV0KdPSNu9tra/Z2Zo2Y6YGOjZ050jIhVCw5oiR6OsLFi1CoYPDwk4QLGeD2sM5qab3GrJpKQKbUa4e0+aURiogktLBPd0BfeghSud4QWDx2evDJzn7SXp9aR5Q53esOCwp+aycP2OQK9TuFIUHm9T8PEpCSxYt51FG3LZF7zgIBr2F1/4CBT2uk3MKNyY3XtfwSbNyA6s4Mz6eSeTpmfjsz4Wrs8NtC8mOirQ25aaFE/9OjVCetq8z6a0ArJlfv/hes6KCLtlVfCiAxWvFalQCmciR6NFi6B/f4iJCdQZg/ABxxgDAwbASy9VeDOKzqMKvve9Qzpx4rgPAsd6AcYLDY99tJxd+/NJS0ksMRiMOadjyER1r/p+uOsWDWJZYWqdjTytHRbLy/PW8f367RhjePfm5JBhRoC6NWuwf29+2PfsbaW0YN12msbWJLZmdOB9eTXFdu3PD7yfe4d04oGZy0ifm0P3Ng0ZcVpbvs3ZHtLe7m0akjYssdgwZNEacAf7HZR4bocOrgTKCScE3lO4LavSUhIxBQWwYIGbvygiFaLSwpkx5iUgBfjFWtvF/1xj4E0gHsgBLrfWbg9z7mDgCSAa+Ke19m+V1U6Ro1J+PtQo/L93mT0fNWq4FXqVINy9w/UkeRuPB/egpc/NcWGkyFKBiRlZgbDy+OyVgTlp/7+9+46vurr/OP46WYSRsEeUEbUICSpokelGHAy3/hytgKvgxtbWRVBwtLWKdY9qsI5W62glYGVoK0NRRNSSICKEIVshYUO45/fHud/LvTf3JqzkfpO8n49HHiR3nnu5wJszPh+A+ycuiPm48WbKDm/ZkBYZZfTIbsq85Rs56ciWdG7dkLTkpNjNw4ENcYJZekoSL3+yjM+KN4QOBWzesZuM9D0nJXse3jw0Y5aZnkpycnJEeIrVTurd6/tEBLJwe9NyqdLf/2jXXgtPPw3/938V7jkDyCtbiGnXDrp0qXQcIrJ3qnLmbDzwJPDXsMvuAKZZa39vjLkj+PPvwu9kjEkGngL6AyuAz40x71lrI//bJiLxtW/vljathbA9RuFCMx/GuArw7dtXyVBiPbe39OgFMS9AAaElSq+rQPgm+qF9OoT6XmLcbb0lOy/EeUuSQ3q3j9gD5i2lHp/dNOLyr1eUsr0sQPH6rWwvC/Dtmk1s2xWgc2vo8eA01m/eSbMGqVzRsz2vzV7KT1tjB7POrRuxYI1r5VS0ahNd22aya7cNjQf2nCL1Xl94OIoOT+HGFBRWeBKzMpX+/kc77zz47W/hzTcxl1wS0aczfCauZdk2zD33QF7efo1LRGKrsnBmrf3YGJMddfG5wCnB718G/kNUOAN6AIustYsBjDF/D95P4Uxkb514IgSr89vjj6945mNQLua559xsyUFW0axLblYG9wzoHHcvVlJSEqMHd4kIZ6MHB2dnjJt18kIOEBFshvRuT5KJDDL1kg2FqzZRuGoTV/Zsx98+X86uAKG9ZN6v23a5X70DA8lJrgPB1KK1tMyoFzececHMY4yhcFVkI3Rv5ix8aXLclIWUbNtZbnYwPTWJ+feeEVrynL34R/rntmZk/zjV/OOodOYrVkBLS3PlNM48EzZsYORVV2FTUiKXUzsmYy6/zp3svOSSfRqTiFSsuvectbbWrgKw1q4yxrSKcZtDgeVhP68AesZ7QGPMdcB1AO2r6H/+IjVOUpKrzn/33Zj3348785GZnoqZMsW16bnggoM+jFBdshjPnVEvORQ8crMyQv0jvSDSr3Orcu2GxhQUcs+AzuVKT0TPOiWZJPJnFYf2tvV86EPWb94Z6n3519nur5i0JNhZcY90dgev9yr5763tO8vIycoILW+CmzkL739praVk286ITf9fLP2Rr3/YxPZdrkH6hBv7MnvxjzGD3d6o6PegooMEdOsGH30EI0bAmDGYIUNc66atW6GgAPPNN66f6q236kCAyEFWpXXOgjNnBWF7zjZaa5uEXb/BWts06j4XA2daa68J/vxLoIe19qbKnk91zkTCeFXe69eH/HxsRkZknbNAADNpEgwbBu++6w4QVJF4Nda8vpXhS5zekmd4NftRA3NCJTS8tkZeUdZBT8zgxy07WVO6I/T43do15th2Tckb7ELIeU/O4KsfSsuNKz0lKeIUZjzRS6SV6dy6YdxSHbHqmp3/9MzQKU2AK3u1Y87SjRF75CrcwB8Ut5ad9/3XX7uDH8XF2KQkTJcubsa0Q4d4D+n873/w+uuu8Xn9+tC7N1x0EdSrV+l7ISLxxatzVt0zZ2uMMVnBWbMsYG2M26wAwrsHtwVWVsvoRGqT1FQXum68EbKzMZddBmef7f5xXbQI88ILrpXTP//pugRUgfDK/rF+Htm/E7f06xja/B9+kjAzPYWehzcno14KFzwzi2MOzaRpsMDs8Q9+SLum9VlZsj1U7X9I7/bce85RoSW7bu1C/w+kW/smMcPZ3gQzgM+Ky51bqlCvI1qQlpLMwrWb2b5rz3PkZmWQmZ4SEaCMMbx7feSJ0HvPOQogYi9eZcGswir+ndIxv/wlfP+9C2OnnooJBGD6dNfMvF8/18w8MzP2gx91lE5jilSj6i5C+x4wJPj9EOBfMW7zOdDRGHOYMSYNuDR4PxHZV/XquX90v/4aWrSAp56CBx6Ajz+GsWNdLbR9CWarVrn79e/vaqKdey68+qpreB7GWhtR+NT7ChW+DRO+b8yTNyiXkf07hTb8z1tewl8/Xc7gY9qEGpB//UMp6zfvpEWjtFAw85bshvbpQOP6aaEgOGpgDk3SYzc+b9YghU6tGsR9ycmGiKVJT9e2jWlSP/b/b8fPWsrXP5SyfVeA3KyMUMHZwlWbKN1eFlEMONZm/YFPzGDMhMjLzn96Zrn+lp54xXrzZxbD8mXYE06AwYOhuNg1Mj/vPLeMPW4cLFsGzZq5gLZ5c8zHF5HqVWXLmsaYv+E2/7cA1gCjgX8CbwLtgWXAxdban4wxh+BKZgwI3ncA8BiulMZL1toH9uY5tawpUkWWLXOn9yZNgvPPh8sug0aNYPlyyM93ddWeeQYuuMDN4GzbhcWdtPROWH65fAPzlpfEXNYL36AOkUt4XrumWK2PAG469Yhg38g9G+xLt+0KldoIBAIMfHw6RavLB48UA8NPPowZi35i5cZtrNu8M9S5snG9JHbs3jO7lpYEw085gsnzV7NgzRZaNEpj/eadobDoLcPmtGkU8VyLHzybpKSkiJmsW0/v6EqARL1PWCj4ZlVoNnBo7w4Ys+cEqlfgNtYMWtz38dEbMQMGwO23h25XbukT3PJ2w4YuwItItYi3rKnemiIS3+TJ8Oc/w9SpkJ7umpQvWeKWR2+5BXoGz+p89hmcfz72kUcY0/BodzIw2PMxuhSGd+IyfOktbt/GQXsCVvgSX7j0lCT+d29/UlJSYj5eRcEOoEn9FDZu27N5v15KEjuiljsb10tibt4ZJCcn8+jkb5lSuJqM9FS6HNqYRmnJTFuwln6dW7FpR1mwC8Ge/WPRQTM6lHVt25iduwPUS05i3ooShvbuEBHQIH7/zOigFf0+Lbm8Hebss2HpUkhJqXjps0sjV6ts6dL4y5siclCpt6ZIbbRsGTzxhGte/sgjMG/ewXlca+Guu+D666FtW/eP9urVMHcudvFiF8rOPx+ee84ttfXoAe+/j7n+evJ6tQr1fIwORV4wC+/rGOskodtzlhoKZvdNmB93qNvLAnR/YBqPfLAg+Hgp5LTJCPWBHD9rKekpewLM0D4d6NymUejnjcGCskWrNtGsQWq5YAaEglkgEGDTjjKKVm+my6GNGTUwh807d1O4ahObdrjHmbe8hGF9st1SZp/I3pmAW34M9sMc2qcDX60ooWjVJhfM+nRg9Dld+OyufhHPnzc4l9GDu0QEs+h+qYFAgEFPzIi43+xRf8JefTUEg2u8pc/S7buwWVlw2mnwj3/Efa9FpHqofZNITfTtt26Zcfp0F5IOOcTNeAweDO3awf33u39o99ef/wwTJ8Ls2TB4MHb0aEz9+qGZl1E33UTS4MHYk0/mjWU7WHXKWYzsfwycey5m/HjyfvObmAVVx0woDM0YeX0dvRZMsVoSPTp5AVMK14SWCeunJoXqkAHktGnE4vVb2LitjE07yggEAvx34bpyZS+2l1kapCZxSfd2jB7chUAgENHiyfPT1l0xZ84GPTGDgptO4P6JCyhcVVKuL2huVgZFq0rZURZgWJ9sMtJT3D4y61ovZaanhHqGxqrL5vGui6795nVE8AQCgYh+mdGnXAtuOoGxE4soeec73sw9jkuC72+lbZy6dnX70kQkoTRzJlLTzJ0LJ50EJ5/sZs5efNFt0n/8cbfkeNttcPnl8MYb+/f427a5QwNvvQU//sjmhd8z1h4WEQgGPTGDRxft4tVr8+j2/KOUbtvpZnB+9Svs+PHlNrPnZGUAhGbThvbpELHvLFZLonFTvmVq0VqKVm8ObfqvlxL5V1bR6s3sKHMzR14R1+27Ynckv7h721DAuX/Sgpi3AULBrF7YbFvR6s38/P6p5M8q5vt1W2L26dy0vYx5y0tCM1TjZy1l/CdL6da2MSXbdoVmqIByByA8Ax+fzvlPzQwtyy55aADd2jUhf1axC7bBAwQu6Lkm6Pkzizn8rvcjgpl3yKJdywwapphywTdcxCnQ3bshOfbBCRGpPpo5E6lJNm+Gc85xfQ8vvBCI2neUkoK98EJMp07u9F2XLq4Mwr544w23bNmxI3bqVNYd0oGXPl2OTU5m1MCcUEHUwlWbwGbxeXoSeY1/dHuYOnVi55Kl5M9ypSy6tWsc0VLJE29Tu8cFnLKIemcV1Rkb2qcD+bOKyz1POO/5wjsUxOq1CdA4PZnBx7Th1c9+CF3mLX+e3aUV/5i7MqJERk5WBsd3aEpaSjLjP4lcyvV+9maogHLLtEP7dGD24h9DQdQLr+Bqts1bvpEvl2+IGL9X/y18Bs4LZt7rzenXi9wNi0LXV9rG6eOP3VK2iCSUZs5EapLXXnP7u4LBLHrfUahcxep6bsP+44/v+3N8+GGoW4BJSyM7M63cDE2IMbT45aWYjz5yP5aVEUh1e8jevb4Powd3YdSgHHKDM2ee6DFHC997Fh2ghvZu7042hol+jJw2kc+Xk5UR2sPm7XE7rVNLmjZIjbid93PJ9t18sayEow+JfJxf9DiUz5dujAhm4ParvfzpMrq1bVzutXi8sHX+07MYP8vNqOUE976Nn7WUotWbyWnTiCt6tg+FV2MMowd3YWifDsxbXsJhd06KCGaxWl+Fvxfm2mvdZ2bz5nKHJZY8NCD0+zqmoBA7fz4sWODKo4hIQimcidQkzz4bmtmodIP3VVe5zd2l5YuvVmjLFsgIhpLcXMz8+eT1zYp782krt2O9+ljTplH/+J9HLJWNnVgUqvR/S7+fhfZreZvkY9U+A0L1yaLNXvJTaGl0aJ8OdG3buNysWvSes6JVm/jvwnXcN2E+t57eMbSRf8PWXbRolMaVvVzd6w1bd5GTlUHzhqkUrd7MNysjH+fVz35gwerNJMeY9Dvm0MwKZwP3BFIXnrq1b0LBTZFdGQpuOoGR/TtFPI4X0MJ5wSxu0PICWrt27mTtrbdiIO7hi6ZJAcz118NNN7m+miKSUApnIjWFtfDNN26/GZGzS97JxIgSFFlZ7h/nJUv27XmaN4eVwaYcLVpgBw5k8u2/j7hJeGHV5f9bxLR1u10gePppGDEiYo+TFwhcQdk9S5UZ9VJCAaN0+65ys1+xTh96NcRysjLIG5RL3qBcjg12AmjRKI1F959Ji4Zu9qtp/RRuOvWI0Czbt6s3MX7W0tB+uIKvVwEw8Kg2odkpcEHuxy27KnyLdtvys3NlAev6efbJjpjZ837On1nM2IlFvDOiD0P7dGD8rKXlDiXcP2lBufch1lLk2IlFZNRLqfCUa8hzz7n2S7/8JSNzGpTb65d3ONz8xxvdoZI77qjwdYtI9VA4E6kprHVfYRu2K93gnZICZWX79jwXXQTjx0Nw8/nz3c/luL+/wG/blYVmvgpXbWLsxCJG9TuMSxbNZPmpZ2OeeQbWrsUOHhzxcLee3pG8QbmhTereUuXjHy4qV89sz0u1oRm38CDo7clKS3JLfn+etogvl22gaYNU1m/eyTlPzWLjVhesNmwrY9qCtTRKSyYJV3KjRaM08me55dn1m3eS06YRXy7fyIXPfhJ3k348PQ6LaAvMj1t2MqxPNnmDc2lc3+0bG9Ynm8z6qYwe3IVhfbPJqJcSsyOC9/qiZ74qWorctKMsoom691kIL7cBuGLB06a5DhHHHIM5/3zXJWDUKNfGqX9/N7v22muQpH8SRPxABwJEaoqkJFdzbP58OOYYoJIN3tu2ubIIbdvu2/Ocfro7eDB5MubMM9napSsfj7iTEWN/hRk3jlt+dT5jp3xPZnoqSS+/TINuRzNswYfwt7/x0pgXWf7Bd7GLnPY/MhQgwjexh982csYtJTTjFgo0FuYu28BXK0q4b8J8DCbUM7Npg9SI/WnJxp2i/H7dFgJAvZSkiMKuAD2ym/Hyp25JdGDULJ0Byu+G2+PlT5aFCtd6M3o2eI+R/Y8kEAhE9BHNqJccKvcxtiBqr1hBEfcM7OwOW6wsjTnzGD5DBm6JMikqTMVdVm3YEB57zJVYeestN5uamur6rp5zjvteRHxD4UykJrnqKrdM9dRTFVbXB8hb+ynmhBOgdet9e46kJLe37fLL4b33GNm/J/b02zGDesB995F0223kDRiAWbfOBbi0NDj8cOysWSyfu3HP80dV+/dmg2KFyYx6KWzaURbREaB0e1lopskLbvcM7ExSUlK5NkXg9ouF2x1MVjvKAjRrkMrsO0+l46jJEbd5+dNlDOnVns+Wbojonxmr1lksRcGZvdNzWtPriDIy01NDfUW91xMIBLDWMrVoLYWrNjHhq5URbZ+aNkglf1YxE752l3vv1Z7m8EdGbvKPMVu61xo1gqFD9+++IlJtKgxnxphMoKW19vuoy4+x1n5dpSMTkfKuvRaOPtrt6zrqqLizKi13bsH8/vf73yexXz946SUYOBAGDsQMH+7KaxQUwOuvY5580s3K3XEH3HwzNGuGAfIOOQSIXeQUXDHV/FlhYXJC+bIWGfVSmFq0JnSIIBAIMHZiEZPnr6Zx/VQm3Ni33OxbZWbfeSrnPv1JzOuMMfTPaR0Rzv43+nR6PPRRucAXS+GqTfQ8vDmjBuZw2QuzeW32Ulo2SnMzaQHL7CU/UfzjFrbtCoRm2mDP4QPv5/Wbg8uiUUu8gGv5FNZ2CYiYkRSR2iVuODPGXIJrPr7WGJMKDLXWfh68ejxwXNUPT0QiHHKIa9d05pnw9tuM7N+rfHX9YxphLrgSzjsPzjhj/59r4EDXiWD8eDdjt3ixm1XLzXXLYZdf7pbLwlS0bDluykK+XL4hFEAALJZu7Zpw8pEt6Hl484j75WZlRJxKbNYglR82bqf7A9M455j4p0djOeq+qewoC5CblcHx2U0jTncWfL2SgVGPN/ipWXGDWVoS7AybVPPGCa410/rNO1m/eSed2zSKqHnWuU0jCm7sG3EIoCiqTIjXrD3899RaS8m2naFWWLFmJCs6JSoiNU/cxufGmHnA2dbaVcaYHsBfgbuste8YY7601h5bnQPdG2p8LnXGO++4gNSxIwwZ4kJbSYkrnTFtGvzud+6rmv/RDl9q9YTX5MqfWRxqfu7dLrwZ+mF3Tor5uMP6ZnP32Z04/sEPI0JT+PKjt0zoSTZuadO7Tb2UJC4+LotXP/vBnaa08OacFWwN6ygQXgw2PTWpXD2zeLyZQGstA5+YUS50AQzp1Z6vfiiJaIoe63Ey01Mo3V4WuW9vQiFfLt8Qt6G6iNRM8RqfV7SsmWytXQVgrf3MGHMqUGCMaUvF+2RFpKpdcIHro/mvf8Hbb8NPP7lZLG85MjOz2odU2R44b3Ypf+aehugRS55Re9HCeSFkzt39ImaedgRPYLZsVI+M9BS6HNKYv3+2lJ0By4iTDmfLrgAZ9ZJ59dNlpKcm0zyjQUSQAiIr+lvXqskLei0apbF+887QsmuLhmms3xJ5qMArC+Jt/o+eHfN4Bw86t25IvZTk0EEGgKG9O2CMKbfEG3oPgyU6wsOZgplI7VVRONtkjDnC228WnEE7Bfgn0KWC+4lIdUhNdWUvLroo0SMB9u5kYawlTyjfkshr4u0ZU1DIPQM6M/jJmeWed+DRbXj5k2Wh+4Lr4bllVyB00vOWfh1JSkqKWDK01jJv+caIxxr/yVJy2jTix+CpzkOb1GfQMVnkDcplbEERXy7bwPotO+natjH/vKFvaNw9DmsW6nsZXZstXNMGKSxYswWArm0bs3N3gNQkw/hPloZKb2Skp9DzsOYR+/aG9ukQOgka/p4ooInUThWFsxFAkjEm11pbCGCt3WSMOQu4tFpGJyI1ineyMLr2VnhZjXBewMhMT2Vonw6h5c/CYHmKM7q0oXR7Gfkzi/nXlz/w09Zd5U5S/u2z5XQ9NDPmIQRvHI9/+H3EhvpAIBB3+bFotet2MKR3e+49Z09f0rzBuTw29TvAVfcHQjNw85aXMG7Kt7w+e3loZq1zm0YsCD6WZ8PWMlo0SmPQ0VmMPqcLgUCA+ycuICnJ0Lh+Gree3pHHpn5XLoh5S60xT+UqoInUOnHDmbX2KwBjzP+MMa8AfwTSg792B16plhGKSI0SHRTCg1m8Jc/M9BQMJjT7tqfC/p7Ztw/+t5p6KbtDG/vfu6EPXe6dwo6yADt3R+4NCw8s4W2uvOvGTiwK1ifLAGwokIXzgln4Sclb+v0stDl/3vIS3r2+DwbDvOUbsdayZacr+Nu5TSN6dGhaLpyBO5Xpje3+SQtCS5a3nt4RIGLzv8fru+kVnQ2fkVQwE6l99qbOWU/gD8AsIAN4DehblYMSkdqloiXPjHpuA3z+rGIw7CmxESy5Ae6y0YO78MgHRUxbsI7CVZv42T0fAK5NU72U5IjnO//pWS44BfeBec8VvVSIjdpzFua8p2ZybHCGbPyspW5GEMOXy9xS6LzlG0MHGIb1ycZi+WpFCZ1buxkzL5g1qZ/Cxm17ujTkZGWQP6vYvV4iZ/m854il5+HNY85IikjtE/e0ZugGxqQBDwD9gUbAPdbav1fD2PaZTmuK+Ft02Yfw/V+xTnnGCiCBQIDD73q/3GN7ASl02CDYSil8Bi38NOiShwbw2NSFTClcE7G/LTcrg5Qkw9fBDfveLF74bNaQ3u0jynEseWgAAPdNmF9u1st7DIMpd+LSu2/4axw3ZSGl23aFwpv3WjLrq6aZSG0T77Tm3jRS+xzYBhwPnABcZox56yCPT0TqgFhLnt6v3mZ+T3jfSI/XczNci0ZpgDsEMH7W0oieluHBLHq/230T5lO6zTVi79auSeh+has28fMOzRjSuz3d2jVh/Kyl5QJXkon8qzPUDzPG/3VzvSbtg3N5e3hvcrMyYt836NbTO5bbc2axoWVPEan99mZZ82prrTcdtRo41xjzyyock4jUMY9O/papRWsiLhv0xAxOz2nNbWd0Aiov1eHxaqZFB7NY9+vWrnFohi3EuL1cXhiKVXstf1ZxaMN/blYG+TOL+fT79eX2roU3iY/ufOCdSg3fCwcurI2ftbTcWLWUKVJ3VDpzFhbMwi/TYQAROSgCgUAotORmZbD4wbNDoWZq0RoCAbfZP9a+tVEDcyqdiTLGkFEvudz9hvXN5uQjW4WWPsP3p3nLh/dNmB/x2EN6t6drW1dDzmDo2rZxaEnUC2Y5wdcwrG926DV5Tc+9n8NPpUbXSYu1N88Vp9Xmf5G6Qo3PRSShkpKS6J/rmrMXrtoU2k+Wm5VB/9zWJCXt+T9keKkOb4nTm4kKn2WavfhHCm46gaSkJMZN+ZapRWvp17lVxP0y01Ni7uHybnP+07OYt3wjXQ/NZGfAUrRqEy9/soyubTPJaZNB0epNDOuTzVcr9uwhG9qnA3mDckM13cAdeLjtjE6hwJg/szj0Gr0ZtHiv0RuPZsxE6pa92XMmIlKlRvbvRMFNJ0RcVnDTCYzs36ncbcNDS0a9lIhZpnsGdI5YSty9e3dow/+0BWvZvXt3KMCVbi+j4gNR7rpjOzSl4Ma+5ARn6L5aURoKZtF7w7xyIN748gblhpZlw0+NerwgF+81xvtZRGo3hTMRSbhYG/3HTiyqMDyNm7KQTTvKQgcHrLXcP2kBp+e0ZljfbPJnFnPE3f8OLR0WrtrEEXf/O2LvWfTjez8bY3j3+r4M7dOB8bOWcsTd/y5XsNY7GTqsbzZLHhrgnnNWccSyavTJ1FhFeCs7MS+X4g8FAAAgAElEQVQidY/CmUhNtmULvPACXHeda4B+550wf37l9/OR6A37oaAzszhueAkvLOuFOO8xNu0o454BnSNuP+HGyNKMowbm8NjUhQx6YgaPTv42YhzjpiwEXLDyDhfEMm95SWgZs7K9YfvzGkWk7tKeM5GaKBCAMWPg8cfhpJPgrLOgXj347jvo3x+OPBKef9796nOV9eSMtaQXr7Cst4crehYuuhH5mAmFzF7yE0Wr3WzYrad3ZGxBUajwrReWome6crMyKLjpBMZOLCJ/ZjHHtmtSbkzxxruvr1FE6q5Ki9DWJCpCK3WCtTB0KCxZAq+8Ah06RF6/axc8+yw88AB89BHk5MR8GL+JV6C2svuEl7pY/ODZoeA0rG829wzoHBHMhvRqz8uf7ike623s93Rr14R3r+8DRDZjz0xPCe1dCw+AmekxCsNaC1u3QnIypKcf8GsUkdorXhFazZyJ1DTPPw8LFrjg1aBB+X/wU1IwN90EGRlw/vlQWAgxNp0fsN274YMP4OWXYcUKSE2FY4+F4cOhU/mN/JXZ103wsfZwefXE4s2gRW++73lYs6hw1jj03NEzXbf06xgKZN5pTGOMC2P//S888QR89hmsWeNmNo2B9u3d+zFsGLRooY3+IrJXtOdMpCaxFv78Z/jjH6FBA8ZNWRixZyli39SQIdCwIUyefPDH8fnnbsl09Gi3jPqHP7jvGzSAk0+GCy6A0tKD/7xBFe3h2rTd7TnzZtCG9unA9w+cxbA+2REtkSB+X01wJS3CuxR4gcybKTPGQFERdO0KV1wB//439O7twvNdd0GLFtCmDcydC126wPTpVfZ+iEjtonAmUpPMmuV+PemkiE3xXkDbUyZilyvyMGKEOzBwMH3+OQwYAH/6k/v+mmvghBPg1FPdUurSpdCqFZxxhlveqwLx9nB1a9eEL5dvICkpicz01FBPy8c/XBQqe9GtXWMWP3h2qDSGp1vbxoyftZQxE9x76Z0g9Q4IeM8bsmCBe819+7pZsi++gDffdEvOY8a496F3b3dA47nn4MILYd68Knk/RKR2UTgTqUkWLYLu3SGsmr03Y3TYnZMiykQYY9xtFy06eM+/ezdceqmbHTr/fIDy5SjS0uCZZyA724WUKjKy/5HlNuB3a9eYectLGFNQyK2nd8RgyJ/lapp5Ye3d6/tijKFecuRffzvKdgPw5fIN5YNu9N5ca+EXv4B774V//QvefhvbaU+bKQCbmgoPP4zt1cst/95/P/z2t1X2fohI7aE9ZyI1ibfHKfSjC2jh/SUjAsvBPvDz739Ds2ahYDZuykJKt+8KPacXajLTUxk5dqybVbr33nIb4w+W8GDmlb4wxpQ7wRk+PnCb/eetKGFo7w5k1k8NtY/KadOIectLIir4RxeOBdzeso0b3Xvxs58xrrQppQWFZNRLCdVe87oQ7D7hCm688VzS770XRo2ChQtrxClaEUkczZyJ1CSdOsHs2W7DOXtR2HT27P3anB/Xyy+7mmpQ+bLqz34GxxwDk8o3Dq8q8arwh1ftj1gSHZwb0fOyf26biPuOGuhOuobXPwNg/Hi49lqYMAH7i1+E3oepRWvIn1kcamg+pXANTxZu5vuje2InTICLL4b33qvS90BEaj7NnInUJD16uFmoDz/E9usXsSk+vLckQN7AHMzTT8Ojjx6851+xAnJd+ClXa2zGEjAmclk1JweWL9/vp9vX0hPxwmr08uetp3eMeA3Wumr/hVFdAMYUFIJ1Bwe8+mfGGPeaBgyA6dMxbdpEvA9A6HG80hu5qT0wK1ZA69Zuxk1EpAIKZyI1iTEwciTcfjvm448rLmz63HNuWfO00w7e86emws6de4azZAl501/mumdeotWWDexISaX+7N6Y7dfDeee5mmtpafv1VN6SqdcYfM+SaUrMnpvRJzjLhdXgexS+FBuPVw9t/Cx3mrNb28aRAS811b22jAzYsCHm8rInb1AuZlaZC9U//ugOS4iIVEDLmiI1zdCh7hRgv36MbBcot2yXd1o2I794x52c/Oc/D26Ns2OPhWnT3PfjxmF79ODThWv5xf/dT6dfv8PxN7zCWz0GYZ94Arp1cxvhu3Xb56cJXzId9MQMAoFAKGhNKVwTarkULt4JzvCWSjGXYicUhkKY5/OlGyJfdvumkU/WrRt8+CGceSa8+WbMGTvPmIJC7IcfwtFHw1tvuW4OIiIV0MyZSE1jDDz1FDzyCJxyCuaoo9w/+OnpsHAh5vXXoVcvmDmzfPeAAzV8OJxyCrRogX32WR7749/588IdETNVt89sQOFvziRvwp8xf/0rHHHEfrxEw6iBOcxe/COFqzaFNuh7Dcx7Ht485hLnyP5HRlwe3VIpXtsngKF9OjBqYA6Dn5iBmTePM0vWsNsk823LDlii3serr3b76fLysLffzlPPTiR/qQmNL/zXz9+eSknxChpv3Ypp23a/wqqI1C1q3yRSk+3YAe+84+qN7dgBWVlw+eVw+OFV95wDB8LUqVBUxLjvy2IuPbbeXsrwu4e4chpHHbXf+94CgUAomHki9rTtp+i2T0P7dGD0wBzIz2f1A39ix8ZSFrbsQHJgN73Wf8+cZtksGzqcX4y6Zs/zDh3qSov07MmPfxzH+LEvYrKyIk5rNg9s54JfX8mKn/elx3/+BW+84cKtiAjx2zcpnInIvrn/fteloH9/GDuWR78vY9OOMheYrCUweTIbrh7OwtMG03vMbXD88W4Dff36+/Q04XvIwi1+8OxybZgO9HGv6tmWUf/4AyxezL3dL+WvDY/AGvcc13TPInfWB5z+6hNk3vEbuO02d6etW92hgKZNITsb+9ZbmNtuww4ZgmnaFDtjBmbECGy9epilS11tuAsu2O9xi0jtEy+cac+ZiOybiRNdKYn27bG9e3Pub4fS/r47+WLApdjOnVlz/Uju/fklTL5kODY72y3/ffjhPj1FeIDKjark7+1B2x/x2j61/v0YFhcV88BvnuLlRh0Z0vew0HV/mbOKr08ZTMYXs13/zLfecg/WoIGr+9alC7z+OqZVK3juOUybNpCejunXD5YswfTt6zo7KJiJyF7SnjMR2Tfr18PPfgYDB2LuvZfDCwr47t9z+OeyUh7seR1zD+nMsBMO27P0eMgh7pTiPnCb+1NCe7e8RuaDnphB4apNjJ1YtF9LmzEPDfRqxfb5Uxk//gMaNs6gW7vtodvnDcoF67oGPFY/jZEvvOBmzi680O39S093M4mjRrnQWlwMZWWuZEbv3tCu3T7PGIqIKJyJyL5p1GhPU/P0dMxFF3HmhRcyPGwPV0RwKi11Ddj30cj+nbAWeh7ePPR4BTedEKy8n7rfe87KHRoYP570i85nxEW9sNZSsm0n42ct3VMDDcu85SUc274pduBpmJ073WGLE07Y86D16mlmTEQOGoUzEdk3p50Gb7/t9pJRSeHXkhKYPn2/m6/fdkaniCCVlJR0wIcBIKqB+axZmCuuCF1eWQsoBg0qH85ERA4i7TkTkX0zfDi89BJs2RKxh+ua47NYcllb7m61iQ/e/8zV93rxRTj7bLfMt5+ig9iBBrNytm93+8fCHr+iFlA0aADbth3cMYiIhNHMmYjsm44d4Zxz4LLLMP/4B4eWruP1hW/R+8UCTKtWXFO/PpcvWsLW91tg1q+EGTMO7Pl27nSnPXftgjZtoEmTg/M6PK1aub1iQZW2gFqyBPr0ObhjEBEJo5kzEdl3Tz/t2jJ168Y1N5xH7+ymmE8/hfnzMe+8Q4Ph19Lyp9XQti2MGeNqsO2rZcvgzjvdpvp+/Vwg7NDBbcbfx9OfFbr8cvjLX8DauKc5Qx0FfvoJCgrcGEREqojCmYjsu7Q0d0px5Uo45BDM669jr7gCunaFY4/FbNuG/eILmDfPFWodPnzfHv/DD6F7d7fkOH26m9lauNA1Xu/fH667zp2aPBh1Gvv3d4cWJk2qvAXUuHGutpn6Y4pIFVIRWhHZP1dd5Voz3X03L/51GqxZzVWndcZ07oxt0CDYpDyVkX3butt9+CHk5FT+uF995QLTm2/CKaeUa9NkrcVs3Oj6Wg4cCKNHH/hrmT7dzYa98Qacemrs53zqKXj4YfjkE1ceRETkAKkIrYgcPD/9BO++C9ddh7WWFU3bMPbHJoxZmR4KZvkziyndvgtbv77rRfnMM3v32KNHu69TTmHclIWhBuWwZz/YuDnr4L334LHHXN21A3XiiS6YXXopDBqEmTgRli6F77+H/HxMjx7w3HPwn/8omIlIldOBABHZd7NmQY8e0LIlBmI2E48oP3HxxXDZZZU/7rJlbhbrtdew1lK6fReTJ33GiW8+x6kNd/LZD5soDbQmZdgV2NYdMeeeC/n5cPvtB/6aTj3VbfZ/4w34wx9cOEtJcb1Bx4xxM3UH0DZKRGRvKZyJyL7bsgUaNw796O3NCu9XGVF+onFjd5/KTJ0KZ50FDRtifviBvL/cxe0ffcwbR55IXrNDSdvdgF+VzqHjiFcwRTe70PfoowcnnIErkzFsmPsSEUmQav9voDGmkzFmXthXqTHm1qjbnGKMKQm7TV51j1NEKtCsmTsMEBSv/ERoT+vKle4+ldm82TUSX7YM+vTB/Pzn1F+1gvtO/xWvHDeIF48/j46f/Rcze7YLck8+CZs2HcxXJiKScNUezqy131pru1lruwE/B7YC78a46XTvdtbaMdU7ShGp0IknutOTixZVXn7CWnj5Zbjoosoft0kTWLvWzYjdcgt21CjGfFgccZMxBYUEsrNd0/ElSxTORKTWSfQGin7A99bapQkeh4jsi/R0d1rz4YcrLz/xww/u5OXVV1f+uGed5ULX+vXYW24Jhb7crAwWP3h2KPQNemIGj05ftqepeiBQ9a9ZRKSaJDqcXQr8Lc51vY0xXxlj3jfGdIn3AMaY64wxc4wxc9atW1c1oxSR8m6/HT76CB5+mJGnd4zYY+YFtJFHZbj2TXfe6ar7V6ZVK2jZEjp1wiQnk5meQm5WBoWrNjF2YhGjBuaEfk5Zshg7b5573I8+quIXKyJSfRJW58wYkwasBLpYa9dEXZcJBKy1m40xA4A/W2s7VvaYqnMmUs2WL3dFWZs3hxtucHXH0tNd0di//MV93XIL3HUX7G1PzOOOc3vOnnkGLr6YQCDA2IlFEYcNbu6Uzsg/3IC5+Wb4+mtX/HbEiPiPuXixK4UxaZJbBm3c2HUcuO4614FARCQB/Fjn7GxgbnQwA7DWllprNwe/nwSkGmNaVPcARaQS7drBF1/A9dfDU0+5zfwpKdCrl2sOPmMG3H333gczcOHuT3+CkSPh//6PpOnTyRvoite2KV3PyOmvMvKOyzHXXOOeF+J3Cti9G269FXr2dN/n57tZthdecF0BunVz49OyqIj4SCJLaVxGnCVNY0wbYI211hpjeuBC5I/VOTgR2UtpaXDJJe7LWvd1IPXAOnaEkhKYPx9eeQU7YgSB776j0CRTlpTC20edxtOjX+D6EYMxAHPmxO51aa1rG/X997BoUUTpDw47zNVpGzUKBg92baIeeWT/xywichAlJJwZYxoA/YFfhV02HMBa+yxwETDCGFMGbAMutbWpz5RIbWXMvs2SxXLNNXDttXDzzdgbbmBMh1N5/b8LGXr8odxxcXeWBZc41xcUktd6C+ann1xj9GhTpriCtnPmQKNGsVsytWjhljq7dXMnRHv1OrCxi4gcBAkJZ9barUDzqMueDfv+SeDJ6h6XiPjACSdAw4bw1FOYG28kMz2Vy08+kjvCToICNE2ymN/8Bm66KfZM3dNPw69/DY0aMW7KQkq37wodWvDKf2SmpzKy/5HuMZ55RuFMRHxBHQJExF+MgX/8A046CbZuZeTNN2Pr1Ys8CdotEzNsGGRluQMH0UpLYdq0iDZQ3oGCvEG5EXXZrLXusdq3dwcYUlOr8cWKiJSncCYi/nP44TBzJvzqV/CnP2GGDIFOnWDXLpgyBfOf/7jTmWPGxJ41W78eWrRwbaDYi96fzZtDvXpur1sLnT0SkcRKdJ0zEZHYOnRwBWlnzoT69V2z9a++cg3Ily2DBx6A5OTY961Xz50WDQpfDvVE9P601h0KSE+vqlcjIrLXNHMmIv7WsaObIdsXbdq4oDVnDnTvHrf3ZyigffQRHHGE2+smIpJgmjkTkdonOdmV0Xj66b3r/fnUU65m2oGeNBUROQg0cyYitdM110C3bpi33iKzSddyvT8B1/szPx++/BLGj0/seEVEghTORKR2atECCgpgwABGDhuGvf76yBOfxzTCPP64Oxk6eTJkZCR4wCIijpY1RaT26tYNPvkENmzAHH00nH6662Rw6qmY445zLZ0+/RQ6d070SEVEQhLW+LwqqPG5iMRVWup6fZaWulZOJ52kAwAiklDxGp9rWVNE6obMTBgwINGjEBGplJY1RURERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETER1ISPQAREZE6Z9s22LAB6teHJk3AmESPSHxEM2ciIiLVwVqYMgXOPx+aN4ef/xw6dIBjjoFnnoHNmxM9QvEJhTMREZGqtmsXDB0KN98MZ58Na9fCqlVQUgKPPQaTJ8Nxx8HixYkeqfiAljVFRESq2g03wOrVMGcONGy453JjoF8/9/XEE9C/P8yeDS1aJG6sknCaORMREalK33wDEyfC22+Hgpm1NuIm1lq46SY47TR4/PFEjFJ8ROFMRESkKj3zDFx3HTRqBMC4KQsZU1AYCmjWWsYUFDJuykIYORJeeAF27kzkiCXBFM5ERESqUkEBXH454IJY6fZd5M8sDgW0MQWF5M8spnT7LmxODrRpA198keBBSyJpz5mIiEhVKimBli0BMMaQNygXgPyZxeTPLAZgWN9s8gblYoxxty0pSdRoxQc0cyYiIlKVMjNh3brQj+EBzRMKZgDr10PjxtU5QvEZhTMREZGqNHAg/O1voR+9pcxwoT1oRUWwcqWrgSZ1lsKZiIhIVRoxAp57DrZsidhjNqxvNkseGsCwvtl79qCNGwfXXANpaYketSSQ9pyJiIhUpa5d4ayz4KKLMG+/TWZ6asQeM2+J84TJb2KmTnV1zqRO08yZiIhIVXv2Wdey6fjjGbn0Y/JO7eD2mFmL+c9/yBufR79/v+7aOwUPD0jdpXAmIiJS1VJT4ZVX4JFHYMIETOvWcOih0LQp3Hgj5rTTYO5cOOKIRI9UfEDLmiIiItXBGLe8edZZsGUL/PQTNGgAzZq560SCEhLOjDHFwCZgN1Bmre0edb0B/gwMALYCQ621c6t7nCIiIlWiYcPIHpsiYRI5c3aqtXZ9nOvOBjoGv3oCzwR/FREREanV/Lrn7Fzgr9b5FGhijMlK9KBEREREqlqiwpkFJhtjvjDGXBfj+kOB5WE/rwheVo4x5jpjzBxjzJx1YRWYRURERGqiRIWzvtba43DLlzcYY06Kuj7Wzkgb64Gstc9ba7tba7u31PFjERERqeESEs6stSuDv64F3gV6RN1kBdAu7Oe2wMrqGZ2IiIhI4lR7ODPGNDTGZHjfA2cA/4u62XvAlcbpBZRYa1dV81BFREREql0iTmu2Bt511TJIAV631v7bGDMcwFr7LDAJV0ZjEa6UxrAEjFNERESk2lV7OLPWLga6xrj82bDvLXBDdY5LRERExA/8WkpDREREpE5SOBMRERHxEfXWFJGaJRCA9eth1y5o3hzS0xM9IhGRg0ozZyJSM6xbBw8+CIcdBjk5cPzx0LIlDBkCn32W6NGJiBw0Cmci4n+ffgpHHw2LF8O778KPP8LKlVBc7C6/8ELIywMbs1a1iEiNomVNEfG3BQvg3HPhpZdg4ECstXtaiDRvjv31rzFXXgn9+0OjRvDb3yZytCIiB0wzZyLib6NHw+23w8CBjJuykDEFhdjgDJm1ljEFhYz7aiMUFMBDD8GGDQkesIjIgVE4ExH/WrUKJk+Ga6/FWkvp9l3kzywOBbQxBYXkzyymdPsubNu2MGAAjB+f6FGLiBwQLWuKiH9NmQJnnAGNG2OAvEG5AOTPLCZ/ZjEAw/pmkzcoF2MMXHEFPPIIjByZuDGLiBwgzZyJiH+VlLgTmUHGmFBA84SCGUCLFlBaWp0jFBE56BTORMS/Gjd2JTSCvKXMcOF70Fi/HjIzq3OEIiIHncKZiPhX//5uz1lJScQes2F9s1ny0ACG9c2O2IPGa6/BoEGJHrWIyAFROBMR/8rKcnvOnn8eYwyZ6akRe8zyBuUyrG82mempmBUrYOJEGDo00aMWETkgOhAgIv52331w8smQm8tIr85ZcI+ZF9DMunVulu2uu6Bp0wQPWETkwGjmTET8rXNn+Ne/4Oqr4eqrMXPn7rlu/XrMww/Dcce5QrW33564cYqIHCSaORMR/+vVC775Bl580bVqKi2FtDTYssX9/M470KNHokcpInJQGFuLetF1797dzpkzJ9HDEJGqFAjATz/Bzp3QvDnUq5foEYmI7BdjzBfW2u7Rl2vmTERqlqQkV89MRKSW0p4zERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxkWoPZ8aYdsaYj4wxRcaY+caYW2Lc5hRjTIkxZl7wK6+6xykiIiKSCCkJeM4y4NfW2rnGmAzgC2PMFGttYdTtpltrByVgfCIiIiIJU+0zZ9baVdbaucHvNwFFwKHVPQ4RERERP0ronjNjTDZwLDA7xtW9jTFfGWPeN8Z0qeAxrjPGzDHGzFm3bl0VjVRERESkeiQsnBljGgFvA7daa0ujrp4LdLDWdgWeAP4Z73Gstc9ba7tba7u3bNmy6gYsIiIiUg0SEs6MMam4YPaatfad6OuttaXW2s3B7ycBqcaYFtU8TBEREZFql4jTmgZ4ESiy1j4a5zZtgrfDGNMDN84fq2+UIiIiIomRiNOafYFfAt8YY+YFL7sLaA9grX0WuAgYYYwpA7YBl1prbQLGKiIiIlKtqj2cWWtnAKaS2zwJPFk9IxIRERHxD3UIEBEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH1E4ExEREfERhTMRERERH0lIODPGnGWM+dYYs8gYc0eM640x5vHg9V8bY45LxDhFREREqlu1hzNjTDLwFHA2kAtcZozJjbrZ2UDH4Nd1wDPVOkgRERGRBEnEzFkPYJG1drG1difwd+DcqNucC/zVOp8CTYwxWdU9UBEREZHqlohwdiiwPOznFcHL9vU2ABhjrjPGzDHGzFm3bt1BHaiIiIhIdUtEODMxLrP7cRt3obXPW2u7W2u7t2zZ8oAHJyIiIpJIiQhnK4B2YT+3BVbux21EREREap1EhLPPgY7GmMOMMWnApcB7Ubd5D7gyeGqzF1BirV1V3QMVERERqW4p1f2E1toyY8yNwAdAMvCStXa+MWZ48PpngUnAAGARsBUYVt3jFBEREUmEag9nANbaSbgAFn7Zs2HfW+CG6h6XiIiISKKpQ4CIiIiIjyiciYiIiPiIwpmIiIiIjyiciYiIiPiIwpmIiIiIjyiciYiIiPiIwpmIiIiIjyiciYiIiPhIQorQioiISC23ezd88AF88QXs3AmHHgoXXwzNmyd6ZL6nmTMRERE5eKyFZ5+Fn/0M7r0Xtm+H1FSYPh2OOAKGDoX16xM9Sl/TzJmIiIgcHNbCb34DU6fCm2/C8cdHXr9+PTzwAPTpAx9/DG3aJGacPqdwJiIiIgfHq6+6pcwZM6BJE6y1GGNCV9vmzTHjxkGTJnDJJS6gSTla1hQREZEDZy386U/w2GPQpAnjpixkTEEh1trg1ZYxBYWMm7IQ7rkHVqyAzz9P8KD9SeFMREREDtynn8K2bXDaaVhrKd2+i/yZxaGANqagkPyZxZRu34VNSoLhw93eNClHy5oiIiJy4ObPhxNPhKQkDJA3KBeA/JnF5M8sBmBY32zyBuW6pc4TT4R33knceH1MMxH0NbQAAAfnSURBVGciIiJy4HbvhuTk0I/GmFBA84SCGbjb7t5dnSOsMRTORERE5MBlZ8NXX4V+9JYyw4XvQeOrr9x9pByFMxERETlw/frBqlUwd27EHrNhfbNZ8tAAhvXN3rMHLRCAZ56Bq65K9Kh9SXvORERE5MClpMD118Mdd2AmTSIzPTVij5m3xJmZnop54w3YvBnOPDPBg/YnE5perAW6d+9u58yZk+hhiIiI1E27dsF550FaGrz0ErZJk8g6Z7t3Y155BX73O5gyBY45JoGDTTxjzBfW2u7Rl2vmTERERA6O1FR4910YORIOPxxz4YVwxhkurH37LeaFFyAzE6ZNg6OOSvRofUszZyIiInLwrV0LL74Ic+fCjh2u8fmVV0KvXhA2m1aXaeZMREREqk+rVnDnnYkeRY2k05oiIiIiPqJwJiIiIuIjCmciIiIiPqJwJiIiIuIjCmciIiIiPqJwJiIiIuIjCmciIiIiPqJwJiIiIuIjCmciIiIiPqJwJiIiIuIjCmciIiIiPqJwJiIiIuIjCmciIiIiPqJwJiIiIuIjCmciIiIiPqJwJiIiIuIjCmciIiIiPmKstYkew0FjjFkHLE3gEFoA6xP4/H6j9yOS3o9Iej/K03sSSe9HJL0fkWrD+9HBWtsy+sJaFc4SzRgzx1rbPdHj8Au9H5H0fkTS+1Ge3pNIej8i6f2IVJvfDy1rioiIiPiIwpmIiIiIjyicHVzPJ3oAPqP3I5Lej0h6P8rTexJJ70ckvR+Rau37oT1nIiIiIj6imTMRERERH1E4ExEREfERhbN9ZIw5yxjzrTFmkTHmjhjXG2PM48HrvzbGHJeIcVYXY0w7Y8xHxpgiY8x8Y8wtMW5zijGmxBgzL/iVl4ixVhdjTLEx5pvga50T4/o68xkxxnQK+32fZ4wpNcbcGnWbWv/5MMa8ZIxZa4z5X9hlzYwxU4wx3wV/bRrnvhX+nVMTxXk/HjbGLAj+mXjXGNMkzn0r/PNVE8V5P+41xvwQ9udiQJz71pXPxxth70WxMWZenPvWjs+HtVZfe/kFJAPfA4cDacBXQG7UbQYA7wMG6AXMTvS4q/g9yQKOC36fASyM8Z6cAhQkeqzV+J4UAy0quL5OfUbCXncysBpXdLFOfT6Ak4DjgP+FXfZH4I7g93cAf4jznlX4d05N/IrzfpwBpAS//0Os9yN4XYV/vmriV5z3417gN5Xcr858PqKufwTIq82fD82c7ZsewCJr7WJr7U7g78C5Ubc5F/irdT4Fmhhjsqp7oNXFWrvKWjs3+P0moAg4NLGj8r069RkJ0w/43lqbyC4eCWGt/Rj4Keric4GXg9+/DJwX465783dOjRPr/bDWTrbWlgV//BRoW+0DS5A4n4+9UWc+Hx5jjAEuAf5WrYOqZgpn++ZQYHnYzysoH0T25ja1kjEmGzgWmB3j6t7GmK+MMe8bY7pU68CqnwUmG2O+MMZcF+P6uvoZuZT4f6HWpc+Hp7W1dhW4/+QArWLcpq5+Vq7CzS7HUtmfr9rkxuAy70txlr3r4ufjRGCNtfa7ONfXis+Hwtm+MTEui65Fsje3qXWMMY2At4FbrbWlUVfPxS1ldQWeAP5Z3eOrZn2ttccBZwM3GGNOirq+zn1GjDFpwDnAP2JcXdc+H/uiLn5W7gbKgNfi3KSyP1+1xTPAEUA3YBVuKS9anft8AJdR8axZrfh8KJztmxVAu7Cf2wIr9+M2tYoxJhUXzF6z1r4Tfb21ttRauzn4/SQg1RjTopqHWW2stSuDv64F3sUtPYSrc58R3F+Uc621a6KvqGufjzBrvOXs4K9rY9ymTn1WjDFDgEHAFTa4gSjaXvz5qhWstWustbuttQHgBWK/zrr2+UgBLgDeiHeb2vL5UDjbN58DHY0xhwVnAi4F3ou6zXvAlcETeb2AEm/pojYKrv+/CBRZax+Nc5s2wdthjOmB+9z9WH2jrD7GmIbGmAzve9wm5/9F3axOfUaC4v5vty59PqK8BwwJfj8E+FeM2+zN3zm1gjHmLOB3wDnW2q1xbrM3f75qhah9qOcT+3XWmc9H0OnAAmvtilhX1qrPR6JPJNS0L9xJu4W4EzJ3By8bDgwPfm+Ap4LXfwN0T/SYq/j9OAE3jf41MC/4NSDqPbkRmI87SfQp0CfR467C9+Pw4Ov8Kvia9RmBBriw1Tjssjr1+cAF01XALtxsx9VAc2Aa8F3w12bB2x4CTAq7b7m/c2r6V5z3YxFu/5T398iz0e9HvD9fNf0rzvvxSvDvh69xgSurLn8+gpeP9/7eCLttrfx8qH2TiIiIiI9oWVNERETERxTORERERHxE4UxERETERxTORERERHxE4UxERETERxTOREQqYYz5tzFmozGmINFjEZHaT+FMRKRyDwO/TPQgRKRuUDgTEQkyxhwfbDSdHqw2Pt8Yc5S1dhqwKdHjE5G6ISXRAxAR8Qtr7efGmPeA+4H6wKvW2prZ/kVEaiyFMxGRSGNwPQu3AzcneCwiUgdpWVNEJFIzoBGQAaQneCwiUgcpnImIRHoeGAW8BvwhwWMRkTpIy5oiIkHGmCuBMmvt68aYZGCWMeY04D6gM9DIGLMCuNpa+0EixyoitZex1iZ6DCIiIiISpGVNERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER9ROBMRERHxEYUzERERER/5f4B6iXbju+e/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画出异常数据的点\n",
    "fig4 = plt.figure(figsize=(10,10))\n",
    "original_data = plt.scatter(data.loc[:,'x1'],data.loc[:,'x2'],marker=\"x\")\n",
    "anomaly_data = plt.scatter(data.loc[:,'x1'][y_predict==-1],data.loc[:,'x2'][y_predict==-1],marker=\"o\",facecolor='none',edgecolors='red',s=150)\n",
    "plt.title(\"anomalu detection result\")\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.legend((original_data,anomaly_data),('original data','anomal data'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#修改阈值\n",
    "ad_model = EllipticEnvelope(contamination=0.02)\n",
    "ad_model.fit(data)\n",
    "\n",
    "y_predict = ad_model.predict(data)\n",
    "\n",
    "fig5= plt.figure(figsize=(10,10))\n",
    "original_data = plt.scatter(data.loc[:,'x1'],data.loc[:,'x2'],marker=\"x\")\n",
    "anomaly_data = plt.scatter(data.loc[:,'x1'][y_predict==-1],data.loc[:,'x2'][y_predict==-1],marker=\"o\",facecolor='none',edgecolors='red',s=150)\n",
    "plt.title(\"anomalu detection result\")\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.legend((original_data,anomaly_data),('original data','anomal data'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyCharm (Learning)",
   "language": "python",
   "name": "pycharm-bd849f12"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
